The approach to restoring an acquired image which is degraded or unclear either due to acquisition device or subject movement during image acquisition, divides in two categories:                Deconvolution where an image degradation kernel, for example, a point spread function (PSF) is known; and        Blind deconvolution where motion parameters are unknown.        
Considering blind deconvolution (which is the most often case in real situations), there are two main approaches:                identifying motion parameters, such as PSF separately from the degraded image and using the motion parameters later with any one of a number of image restoration processes; and        incorporating the identification procedure within the restoration process. This involves simultaneously estimating the motion parameters and the true image and it is usually done iteratively.        
The first blind deconvolution approach is usually based on spectral analysis. Typically, this involves estimating the PSF directly from the spectrum or Cepstrum of the degraded image. The Cepstrum of an image is defined as the inverse Fourier transform of the logarithm of the spectral power of the image. The PSF (point spread function) of an image may be determined from the cepstrum, where the PSF is approximately linear. It is also possible to determine, with reasonable accuracy, the PSF of an image where the PSF is moderately curvilinear. This corresponds to even motion of a camera during exposure. It is known that a motion blur produces spikes in the Cepstrum of the degraded image.
So, for example, FIG. 5a shows an image of a scene comprising a white point on a black background which has been blurred to produce the PSF shown. (In this case, the image and the PSF are the same, however, it will be appreciated that for normal images this is not the case.) FIG. 5b shows the log of the spectrum of the image of FIG. 5a, and this includes periodic spikes in values in the direction 44 of the PSF. The distance from the center of spectrum to the nearest large spike value is equal to the PSF size. FIG. 5c shows the Cepstrum of the image, where there is a spike 40 at the centre and a sequence of spikes 42. The distance between the center 40 and the first spike 42 is equal to the PSF length.
Techniques, for example, as described at M. Cannon “Blind Deconvolution of Spatially Invariant Image Blurs with Phase” published in IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. ASSP-24, NO. 1, February 1976 and refined by R. L. Lagendijk, J. Biemond in “Iterative Identification and Restoration of Images”, Kluwer Academic Publishers, 1991 involve searching for those spikes in a Cepstrum, estimating the orientation and dimension of the PSF and, then, reconstructing the PSF from these parameters. This approach is fast and straight-forward, however, good results are usually generally achieved only for uniform and linear motion or for out of focus images. This is because for images subject to non-uniform or non-linear motion, the largest spikes are not always most relevant for determining motion parameters.
A second blind deconvolution approach involves iterative methods, convergence algorithms, and error minimization techniques. Usually, acceptable results are only obtained either by restricting the image to a known, parametric form (an object of known shape on a dark background as in the case of astronomy images) or by providing information about the degradation model. These methods usually suffer from convergence problems, numerical instability, and extremely high computation time and strong artifacts.
A CMOS image sensor may be built which can capture multiple images with short exposure times (SET images) as described in “A Wide Dynamic Range CMOS Image Sensor with Multiple Short-Time Exposures”, Sasaki et al, IEEE Proceedings on Sensors, 2004, 24-27 Oct. 2004 Page(s):967-972 vol. 2.
Multiple blurred and/or undersampled images may be combined to yield a single higher quality image of larger resolution as described in “Restoration of a Single Superresolution Image from Several Blurred, Noisy and Undersampled Measured Images”, Elad et al, IEEE Transactions on Image Processing, Vol. 6, No. 12, December 1997.